A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer
With the development of the global economy, PM2.5 fine particulate matter concentration has emerged as a major environmental issue worldwide, significantly impacting human health. However, most existing research methods largely ignore the spatial characteristics of PM2.5 concentrations. In response,...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10884736/ |
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| author | Yuan Huang Feilong Han Qimeng Feng |
| author_facet | Yuan Huang Feilong Han Qimeng Feng |
| author_sort | Yuan Huang |
| collection | DOAJ |
| description | With the development of the global economy, PM2.5 fine particulate matter concentration has emerged as a major environmental issue worldwide, significantly impacting human health. However, most existing research methods largely ignore the spatial characteristics of PM2.5 concentrations. In response, this paper proposes a new approach based on Graph Convolutional Networks (GCN) and Transformer. To enhance the model’s predictive performance, we designed a new Transformer architecture named FFPformer, which incorporates the Fast Fourier Transform into the Transformer framework. Initially, adjacency matrices are constructed using geographic latitude, longitude, and altitude information to represent the spatial relationships among monitoring stations. These spatial relationships are then extracted using GCN. The extracted spatial features are transformed into value, position, and temporal representations via embedding blocks. The encoded temporal information is converted into frequency domain representations through the encoding layer, then reconverted into temporal information after attention calculations, and input into the decoding layer to produce the prediction results. Finally, a Huber loss is used to optimize the neural network parameters and enhance the robustness of the model. The GCN-FFPformer model has been compared with traditional time series models and advanced Transformer models using real-world datasets. The results indicate that on the Beijing-Tianjin-Hebei dataset, MAE, RMSE, and TIC were reduced by 9.65%, 11.67%, and 12.51% on average compared to other models, demonstrating that GCN-FFPformer is a novel method for accurately predicting PM2.5 concentrations in urban areas. |
| format | Article |
| id | doaj-art-9469f9932bcd4ac9930ff2c5bc062519 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9469f9932bcd4ac9930ff2c5bc0625192025-08-20T03:11:55ZengIEEEIEEE Access2169-35362025-01-0113306133062210.1109/ACCESS.2025.354177410884736A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and TransformerYuan Huang0Feilong Han1https://orcid.org/0009-0002-6533-2519Qimeng Feng2School of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaWith the development of the global economy, PM2.5 fine particulate matter concentration has emerged as a major environmental issue worldwide, significantly impacting human health. However, most existing research methods largely ignore the spatial characteristics of PM2.5 concentrations. In response, this paper proposes a new approach based on Graph Convolutional Networks (GCN) and Transformer. To enhance the model’s predictive performance, we designed a new Transformer architecture named FFPformer, which incorporates the Fast Fourier Transform into the Transformer framework. Initially, adjacency matrices are constructed using geographic latitude, longitude, and altitude information to represent the spatial relationships among monitoring stations. These spatial relationships are then extracted using GCN. The extracted spatial features are transformed into value, position, and temporal representations via embedding blocks. The encoded temporal information is converted into frequency domain representations through the encoding layer, then reconverted into temporal information after attention calculations, and input into the decoding layer to produce the prediction results. Finally, a Huber loss is used to optimize the neural network parameters and enhance the robustness of the model. The GCN-FFPformer model has been compared with traditional time series models and advanced Transformer models using real-world datasets. The results indicate that on the Beijing-Tianjin-Hebei dataset, MAE, RMSE, and TIC were reduced by 9.65%, 11.67%, and 12.51% on average compared to other models, demonstrating that GCN-FFPformer is a novel method for accurately predicting PM2.5 concentrations in urban areas.https://ieeexplore.ieee.org/document/10884736/PM2.5 concentration predictionspatiotemporal predictiongraph convolutional networks (GCN)Transformer |
| spellingShingle | Yuan Huang Feilong Han Qimeng Feng A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer IEEE Access PM2.5 concentration prediction spatiotemporal prediction graph convolutional networks (GCN) Transformer |
| title | A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer |
| title_full | A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer |
| title_fullStr | A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer |
| title_full_unstemmed | A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer |
| title_short | A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer |
| title_sort | novel model for predicting pm2 5 concentrations utilizing graph convolutional networks and transformer |
| topic | PM2.5 concentration prediction spatiotemporal prediction graph convolutional networks (GCN) Transformer |
| url | https://ieeexplore.ieee.org/document/10884736/ |
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